{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d2c59bcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (1.5.3)\n",
      "Requirement already satisfied: openpyxl in c:\\programdata\\anaconda3\\lib\\site-packages (3.0.10)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2022.7)\n",
      "Requirement already satisfied: numpy>=1.21.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.24.3)\n",
      "Requirement already satisfied: et_xmlfile in c:\\programdata\\anaconda3\\lib\\site-packages (from openpyxl) (1.1.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba3ec593",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting jieba\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c6/cb/18eeb235f833b726522d7ebed54f2278ce28ba9438e3135ab0278d9792a2/jieba-0.42.1.tar.gz (19.2 MB)\n",
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      "     ---------------------------------------- 19.2/19.2 MB 2.0 MB/s eta 0:00:00\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Building wheels for collected packages: jieba\n",
      "  Building wheel for jieba (setup.py): started\n",
      "  Building wheel for jieba (setup.py): finished with status 'done'\n",
      "  Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314474 sha256=83f63ac326b2ed4f30e74e1b543b39926097bd9ef93befcfa11a178ef9e387f9\n",
      "  Stored in directory: c:\\users\\administrator\\appdata\\local\\pip\\cache\\wheels\\37\\08\\79\\ea7c0d2ca823affa13f89586a5a9eff8dd6ad589640396e1b5\n",
      "Successfully built jieba\n",
      "Installing collected packages: jieba\n",
      "Successfully installed jieba-0.42.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5ce7ba6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9b24067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>creationTime</th>\n",
       "      <th>nickname</th>\n",
       "      <th>referenceName</th>\n",
       "      <th>content_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...</td>\n",
       "      <td>2017-04-17 13:01:54</td>\n",
       "      <td>鑫***辰</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>安装师傅很给力，热水器也好用，感谢美的。</td>\n",
       "      <td>2017-04-17 10:45:33</td>\n",
       "      <td>切***药</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>还没安装，基本满意</td>\n",
       "      <td>2017-04-17 10:58:33</td>\n",
       "      <td>j***x</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...</td>\n",
       "      <td>2017-10-18 20:22:33</td>\n",
       "      <td>j***2</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...</td>\n",
       "      <td>2017-04-17 09:19:16</td>\n",
       "      <td>j***6</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>差评，差的一塌糊涂，千万别买，上当了，</td>\n",
       "      <td>2016-11-25 14:57:52</td>\n",
       "      <td>沫沫19900404</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>热水器还没有安装，就搞一肚子气，安装人员今天推明天，明天推后天，售后安装服务太差，给差评，目...</td>\n",
       "      <td>2016-11-25 13:39:28</td>\n",
       "      <td>j***l</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>好不容易网购一下还漏电</td>\n",
       "      <td>2016-11-25 13:38:49</td>\n",
       "      <td>K***T</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>东西送的挺快，后期报装2天还没人联系我，售后太差</td>\n",
       "      <td>2016-11-25 10:19:20</td>\n",
       "      <td>j***p</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>买了两个，送到一个，还有一个至今未送到。</td>\n",
       "      <td>2016-11-25 10:17:45</td>\n",
       "      <td>s***8</td>\n",
       "      <td>美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                content         creationTime  \\\n",
       "0     东西收到这么久，都忘了去好评，美的大品牌，值得信赖，东西整体来看，个人感觉还不错，没有出现什...  2017-04-17 13:01:54   \n",
       "1                                  安装师傅很给力，热水器也好用，感谢美的。  2017-04-17 10:45:33   \n",
       "2                                             还没安装，基本满意  2017-04-17 10:58:33   \n",
       "3     电热水器收到了，京东自营商品就是好，发货速度快，品质有保障，安装效果好，宝贝非常喜欢，冬天可...  2017-10-18 20:22:33   \n",
       "4     用了几次才来评价，对产品非常满意，加热快保温时间长，售后服务特别好，主动打电话询问送货情况帮...  2017-04-17 09:19:16   \n",
       "...                                                 ...                  ...   \n",
       "1995                                差评，差的一塌糊涂，千万别买，上当了，  2016-11-25 14:57:52   \n",
       "1996  热水器还没有安装，就搞一肚子气，安装人员今天推明天，明天推后天，售后安装服务太差，给差评，目...  2016-11-25 13:39:28   \n",
       "1997                                        好不容易网购一下还漏电  2016-11-25 13:38:49   \n",
       "1998                           东西送的挺快，后期报装2天还没人联系我，售后太差  2016-11-25 10:19:20   \n",
       "1999                               买了两个，送到一个，还有一个至今未送到。  2016-11-25 10:17:45   \n",
       "\n",
       "        nickname                            referenceName content_type  \n",
       "0          鑫***辰  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n",
       "1          切***药  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n",
       "2          j***x  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n",
       "3          j***2  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n",
       "4          j***6  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          pos  \n",
       "...          ...                                      ...          ...  \n",
       "1995  沫沫19900404  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n",
       "1996       j***l  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n",
       "1997       K***T  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n",
       "1998       j***p  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n",
       "1999       s***8  美的（Midea）60升预约洗浴 无线遥控 电热水器 F60-15WB5(Y)          neg  \n",
       "\n",
       "[2000 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \n",
    "# 读取 CSV 文件  \n",
    "df = pd.read_csv('reviews.csv')  \n",
    "  \n",
    "# 显示数据框（DataFrame）的内容  \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a1a7372",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('output.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f053f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import jieba.posseg as psg\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "path = '/home/kesci/input/emotion_analysi7147'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3fa1fba",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/kesci/input/emotion_analysi7147/reviews.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m reviews \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(path\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/reviews.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m   1736\u001b[0m     f,\n\u001b[0;32m   1737\u001b[0m     mode,\n\u001b[0;32m   1738\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1739\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1740\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m   1741\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[0;32m   1742\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1743\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1744\u001b[0m )\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m    857\u001b[0m             handle,\n\u001b[0;32m    858\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m    859\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m    860\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m    861\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    862\u001b[0m         )\n\u001b[0;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/kesci/input/emotion_analysi7147/reviews.csv'"
     ]
    }
   ],
   "source": [
    "reviews = pd.read_csv(path+'/reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb2abb38",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews.shape"
   ]
  }
 ],
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